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Add paper link, GitHub repository, and update metadata

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This PR updates the dataset card to include:
- A link to the paper: [CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining](https://huggingface.co/papers/2605.00933).
- A link to the official GitHub repository.
- Improved metadata including the `time-series-forecasting` task category.
- Updated citation information with the official BibTeX.

Files changed (1) hide show
  1. README.md +17 -7
README.md CHANGED
@@ -1,13 +1,16 @@
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  ---
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- license: mit
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  language:
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  - en
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- pretty_name: CGM-JEPA Downstream Evaluation Splits
 
 
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  task_categories:
 
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  - tabular-classification
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  - feature-extraction
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  task_ids:
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  - tabular-multi-class-classification
 
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  modalities:
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  - Time Series
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  - Tabular
@@ -21,8 +24,6 @@ tags:
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  - healthcare
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  - time-series
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  - subject-level-classification
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- size_categories:
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- - n<1K
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  configs:
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  - config_name: default
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  data_files:
@@ -34,7 +35,9 @@ configs:
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  # CGM-JEPA Downstream Evaluation Splits
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- Labeled cohort splits used to evaluate CGM encoders on two binary metabolic outcomes — **insulin resistance** and **β-cell dysfunction** — in the paper *CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining*.
 
 
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  > Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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@@ -177,8 +180,15 @@ Released under the **MIT license**, inherited from the upstream [`Metabolic_Subp
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  ## Citation
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- > _Citation block to be filled once the CGM-JEPA paper has a stable venue / arXiv link._
 
 
 
 
 
 
 
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  ## Code repository
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- [`https://github.com/cruiseresearchgroup/CGM-JEPA`](https://github.com/cruiseresearchgroup/CGM-JEPA)
 
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  ---
 
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  language:
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  - en
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+ license: mit
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+ size_categories:
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+ - n<1K
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  task_categories:
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+ - time-series-forecasting
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  - tabular-classification
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  - feature-extraction
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  task_ids:
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  - tabular-multi-class-classification
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+ pretty_name: CGM-JEPA Downstream Evaluation Splits
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  modalities:
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  - Time Series
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  - Tabular
 
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  - healthcare
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  - time-series
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  - subject-level-classification
 
 
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  configs:
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  - config_name: default
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  data_files:
 
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  # CGM-JEPA Downstream Evaluation Splits
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+ [Paper](https://huggingface.co/papers/2605.00933) | [Code](https://github.com/cruiseresearchgroup/CGM-JEPA)
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+
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+ Labeled cohort splits used to evaluate CGM encoders on two binary metabolic outcomes — **insulin resistance** and **β-cell dysfunction** — in the paper [CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining](https://huggingface.co/papers/2605.00933).
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  > Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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  ## Citation
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+ ```bibtex
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+ @article{muhammad2026cgm,
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+ title = {CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining},
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+ author = {Muhammad, Hada Melino and Li, Zechen and Salim, Flora and Metwally, Ahmed A},
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+ journal = {arXiv preprint arXiv:2605.00933},
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+ year = {2026}
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+ }
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+ ```
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  ## Code repository
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+ [https://github.com/cruiseresearchgroup/CGM-JEPA](https://github.com/cruiseresearchgroup/CGM-JEPA)